Computer Vision Solution to Automate Maintenance Process

Business Impact

  • This Denver-based company is a leader in introducing automation to utility pole periodic maintenance in the United States.
  • Their priority was to reduce manual attention/maintenance to the utility poles across Denver and improve the efficiency of their workforce.
  • We have developed a platform with LOW-CODE-NO-CODE to automate the entire Machine Learning pipeline to data annotation, model training, model validation, and deployment within one simple easy-to-use web-based portal.
  • The advances in Vision Intelligence enabled us to build a DIY (Do-It-Yourself) Machine Learning platform to upload utility pole images, tag components installed on the pole, and train Segmentation algorithms.
  • These algorithms will assist the inspection team to automate most of the inspection and take measurements and compile audit reports.
  • Automating Utility Pole inspection using Vision Intelligence is a key differentiator amongst their competitors.
  • This automation has offered tremendous value, cost-saving, and ROI for the client compared to manual inspection, which had been the industry standard.

Technology Stack

Business Challenges

Utility inspection is an expensive and labor-intensive process. A human maintenance engineer, physically visits or reviews, videos/images of the utility pole to take measurements and perform audits.

Solution Overview

The advances in Vision Intelligence enabled us to build a DIY (Do-It-Yourself) Machine Learning platform to upload utility pole images, tag components installed on the pole, and train Segmentation algorithms. These algorithms will assist the inspection team to automate most of the inspection and take measurements and compile audit reports.

Key Features

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Automating Utility Pole inspection using Vision Intelligence is a key differentiator amongst their competitors.

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This automation has offered tremendous value, cost-saving, and ROI for the client compared to the manual inspection, which had been the industry standard.

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The inspection data can be uploaded, tagged and Image segmentation models can be trained by non-technical team members easily in the web-based portal.

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Trained models can be deployed in on-premise or cloud infrastructure.

Trusted and Proven Engagement Model

  • A nondisclosure agreement (NDA) is signed to not disclose any sensitive information revealed over the course of doing business together.
  • Our NDA-driven process is established to keep clients’ data and IP safe and secure.
  • The solution discovery phase is all about knowing your target audience, writing down requirements, and creating a full scope for the project.
  • This helps clarify the goals, and limitations, and deliver quality products & services.
  • Our engagement model defines the project size, project development plan, duration, concept, POC etc.
  • Based on these scenarios, clients may agree to a particular engagement model (Fixed Bid, T&M, Dedicated Team).
  • The SOW document shall list details on project requirements, project management tools, tech stacks, deliverables, milestones, timelines, team size, hourly/monthly rate cards, billable hours and invoice details.
  • On signing the SOW, an official project kick-off meeting shall be initiated.
  • Our implementation approach, ecosystem, tools, solutions modelling, sprint plan, etc. shall be discussed during this meeting.

Our Award-Winning Team

A seasoned AI & ML team of young, dynamic and curious minds recognized with global awards for making significant impact on making human lives better

Awarded Bronze Trophy at CII National competition on Digitization, Robotics & Automation (DRA) – Industry 4.0

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50+

AI & ML
Engineers

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40+

AI & ML
Projects for
reputed Clients

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5 yrs

in AI & ML
Engineering

Awarded as Winner among 1000 contestants at TechSHack Hackathon

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